When it comes to wavelet analysis, a common question is: Which wavelet is best for my signal? The answer isn’t universal—it depends entirely on the nature of your data and what features you’re trying to extract. Wavelets are mathematical functions that decompose signals into time-frequency space. Each wavelet has its... [Read More]

Signals present irregularities such as sudden drops, peaks or subtle shifts that break the pattern. These features mark important transitions wether the data represent financial markets, seismic data, geological scans and others. Detecting of these patterns requires precision and a correct analytical approach. Available algorithms are tailored to specific applications.... [Read More]

Distributed training is the process of partitioning the workload across multiple processing units to speed up the training especially in ML. [Read More]

Image analysis extracts feature information to quantify shapes, enumerate object’s structures and characterize shape of structures. Rock particles result from erosion, blasting, comminution, and others. The geometric characteristics of these particles encode information about their generating process. The mineralogy influences mechanical properties and determines the shape and size of individual... [Read More]

Exploration projects collect information over the years. The information changes due to incoming data, change of personnel, among other factors. Changes in the data must be justified and well documented, it requires a sound database and professionals to provide insightful details. A simple case is to understand the impact of... [Read More]